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INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 34: 1181–1195 (2014)
Published online 18 June 2013 in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/joc.3755
Regional temperature change over the Huang-Huai-Hai
Plain of China: the roles of irrigation versus urbanization
Wenjiao Shi, Fulu Tao* and Jiyuan Liu
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
ABSTRACT: Irrigation and urbanization, two widely occurring land-use/land-cover changes, have important influences
on regional climate, especially on temperature. The effect of irrigation and urbanization on temperature is separately
documented in several studies. However, there are few studies analysing the combined effects of irrigation and urbanization
on temperature. In this study, changes in surface temperature were analysed in relation to irrigation and urbanization on
the Huang-Huai-Hai Plain of China from 1955 to 2007. To better characterize the combined effects of these two processes
on temperatures, long-term weather observations are used along with irrigation and urbanization data sets. The results
indicated that irrigation had a significant cooling effect of 0.17–0.20 ◦ C decade−1 on average daily maximum temperature
of the hottest 1, 5, and 30 d of each year on the Huang-Huai-Hai Plain. Compared with the reference conditions, irrigation
also indicated a cooling effect of 0.12 ◦ C decade−1 on summertime daily maximum temperature. In contrast to its effect
on maximum temperature, irrigation appeared to induce a warming effect of 0.43 ◦ C/decade on average daily minimum
temperature of the coldest 1 d of each year. Where irrigation interfaced with urbanization, the urbanization warming of
extreme daily maximum temperature seemed to only partly counteract the irrigation cooling effect. The findings of this study
deepen our insight into the effects of irrigation and urbanization on temperature dynamics, and the combined implications
for regional climate change. Further efforts to understand irrigation and urbanization effects on climate should not only use
observations, but should also be coupled with dynamic land-use and regional climate models to understand the complex
processes and controlling mechanisms.
KEY WORDS
irrigation; urbanization; climate change; extreme temperature; land-use change
Received 27 February 2012; Revised 23 January 2013; Accepted 19 May 2013
1. Introduction
Two important processes of human activities, the
emission of greenhouse gases and land-use/land-cover
changes, have great influence on climate change (Pielke
et al., 2002; Paeth et al., 2009). Greenhouse gas emissions are embedded in climate models for IPCC climate
change assessments, and land-cover changes in forcing
scenarios for future climate change studies are also
documented (Feddema et al., 2005). In recent years,
there has been growing interest in land-use/land-cover
change and its effect on climate change (Kalnay and
Cai, 2003; Roy et al., 2003; Parker, 2004; Pitman et al.,
2004; Feddema et al., 2005; Lobell et al., 2006a, 2008;
Kueppers et al., 2007; Deo et al., 2009; Hu et al., 2010).
Particularly, urbanization (Kalnay and Cai, 2003; Zhou
et al., 2004) and irrigation (Mahmood et al., 2006;
Bonfils and Lobell, 2007; Kueppers et al., 2007; Lobell
et al., 2008; Lobell et al., 2009; Puma and Cook, 2010;
Wu et al., 2010) are the two main causes of rapid
land-use change with significant influence on climate
change of regional scale.
* Correspondence to: F. Tao, Institute of Geographic Sciences and
Natural Resources Research, Chinese Academy of Sciences, Beijing,
China. E-mail: [email protected]
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Irrigated lands account for approximately 40% of
global food production. Thus food security depends on
the magnitude of climatic changes, especially in the
irrigated regions of the world (Lobell et al., 2006b).
The impact of irrigation on temperature is detailed in
several studies driven by model simulations (Lobell et al.,
2006a; Kueppers et al., 2007, 2008; Diffenbaugh, 2009;
Lobell et al., 2009; Sacks et al., 2009; Jin and Miller,
2010) and observational data analyses (Mahmood et al.,
2006; Bonfils and Lobell, 2007). Several previous studies
also demonstrate that irrigation reduces soil albedo,
increases transpiration and evaporation, and alters local
soil moisture, energy balance and climate. On hourly to
seasonal timescales, the effects of irrigation on climate
change sometimes exceed those of greenhouse gases
(Bonfils and Lobell, 2007; Kueppers et al., 2007; Lobell
et al., 2009). Model simulations indicate that the impact
of land use change due to irrigation on temperature is
most pronounced in summer (Kueppers et al., 2008).
Furthermore, substantial regional differences exist in
the irrigation cooling effect (Lobell et al., 2009). None
of the climate models in the fourth assessment report
(AR4) of IPCC include irrigation-driven changes in soil
moisture (Lobell et al., 2008). In addition, the effects
of irrigation on extreme temperatures are consistently
ignored (Lobell et al., 2008; Hu et al., 2010). Because
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Figure 1. The location of the study area in China (left) and the location of the 95 meteorological stations used in this study (right).
there is a lack of studies directly comparing surface
temperature using observed data (Mahmood et al., 2006),
there is the need to examine the effects of irrigation on
extreme temperatures via observational data analysis (Hu
et al., 2010; Jin and Miller, 2010). The cooling effects
of irrigation continue to attract the interest of research
communities across the globe, including the United States
(Mahmood et al., 2006; Kueppers et al., 2008), India (De
Rosnay et al., 2003; Douglas et al., 2006) and Thailand
(Haddeland et al., 2006). However, there is little work
in this direction for the Huang-Huai-Hai Plain of China,
which is one of the most intensively irrigated regions in
the world (Siebert et al., 2005).
Urbanization induces heat islands, which is partially
responsible for observed warming in urban land surfaces
in the last few decades (Zhou et al., 2004). Changes
in surface temperature depend on the nature of ‘urban’
(Hua et al., 2008; Jones et al., 2008), spatial scales
(Jones et al., 2008; Ren et al., 2008) and seasonal differences among urban regions (Kueppers et al., 2008).
For example, cities with dense populations, buildings and
limited greenbelts exhibit urbanization effects are different from cities with the reverse characteristics (Hua
et al., 2008). There is a wider effect of urbanization on
annual mean surface air temperature in large cities than
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in small cities (Ren et al., 2008). Two thirds of the constituent data come from the marine realm, where any
urban influence cannot be present (Jones et al., 2008).
Consequently, the urbanization effect is hardly noticeable
at global scale. However, urban heat islands (UHI) could
greatly influence natural variations in local climate (Hua
et al., 2008). Tayan et al. (1997) showed that the effect of
urbanization on UHI is most obvious in spring, whereas
its highest effect on the lowering of minimum temperature occurs in winter. Hua et al. (2008) showed that
during spring and summer, the effects of UHI on temperature are relatively weaker in coastal than in inland cities.
Changes in surface temperature could result from changes
in soil albedo and soil moisture (Sailor, 1995; Jacobson,
1999). Rapid urbanization alters moisture flux associated
with deep-rooted trees and concrete. This in turn alters
the local surface energy balance by increasing sensible
heat flux and decreasing latent heat flux (Kueppers et al.,
2008). Other studies contend that compared with largescale changes in land use, the impact of urbanization on
temperature is relatively small (Kalnay and Cai, 2003).
In the rapidly urbanizing southeast China, studies suggest significant effect of urbanization on climate change
(Zhou et al., 2004). These conflicting reports indicate the
need for further research into the effects of urbanization
on land surface air temperature.
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IRRIGATION COOLING EFFECT
Figure 2. Locations of the agrometeorological stations in Hebei Province.
At the end of 2007, the total population in the HuangHuai-Hai Plain was 422.59 million, which was increased
by 206.68 million people as compared to that in the end
of 1995. On the basis of remote sensing and land-use
change interpretations, the Huang-Huai-Hai Plain (the socalled ‘Granary of China’) has had significant expansion
in irrigated lands and urbanization in recent decades (Liu
et al., 2003, 2010). This greatly influences land surface
air temperature and regional climate change (Mahmood
et al., 2006; Hu et al., 2010). The effects of irrigation
and urbanization on temperature and climate change are
separately analysed in several studies. However, there
are few studies that integrate the effects of these land
physiographic factors on temperature change. In fact,
the effects of irrigation and urbanization on temperature
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climate change in the Huang-Huai-Hai Plain largely
remain uncertain. The rapid expansions in urbanization
and irrigated land in the Plain in recent decades provides
a unique opportunity for the combined analysis of the
effects of these two processes on temperature and climate
change.
The objectives of this study were to (1) investigate the impacts of irrigation on long-term (1955–2007)
temperature trends in the Huang-Huai-Hai Plain of
China, (2) determine the influences of irrigation and
urbanization effects on daily extreme temperature over
the Plain, and (3) compare the relative effects of
irrigation and urbanization on extreme daily maximum
temperature.
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Figure 3. Decadal trends in temperature for different levels of irrigation on the Huang-Huai-Hai Plain of China in the period 1955–2007. The
label of on the abscissa shows the irrigation region followed by the number of stations in parenthesis. The decadal trends of T max shown are the
average of the 1 (top 1), 5 (top 5), and 30 (top 30) highest daily T max per year and the average annual daily maximum temperatures (T max_mean ).
The decadal trends of T min shown are the average of the 1 (bottom 1), 5 (bottom 5), and 30 (bottom 30) coldest daily T min per year and
the average annual daily maximum temperature (T min_mean ). The decadal trend of T mean shown is the average annual daily mean temperatures
(T mean_mean ). Error bars indicate 95% confidence interval based on bootstrap resampling. Dark blue bars represent the standard deviation of
bottom 1, and red bars represent the standard deviation of top 1.
2.
2.1.
Data and methods
Study area
The study area is comprised of two metropolises
(Beijing and Tianjin) and five provinces (Anhui, Hebei,
Henan, Jiangsu and Shandong). It is located between
110◦ 1 E–124◦ 21 E and 28◦ 59 N–43◦ 9 N (Figure 1). The
Huang-Huai-Hai Plain, which is the largest and most
important agricultural base in China, also lies in the
study area. Annual average temperature (1955–2007) in
the plain gradually varies from 3.1 ◦ C in the north to
16.8 ◦ C in the south, with an average annual precipitation of 386–2333 mm. Double cropping of winter wheat
and summer maize is the main production system in
the region. Winter wheat is sown from early October
and harvested by the following June, and summer maize
grows immediately after winter wheat is harvested till
late September (Sun et al., 2010). In cultivated lands,
annual water consumption far exceeds annual precipitation, which is mainly sustained by irrigation. Intensive
irrigation therefore offsets not only the water deficit, but
also maintains high crop yield in the Plain. The HuangHuai-Hai Plain has experienced significant expansions in
built-up and residential areas in last several decades (Liu
et al., 2005; Sun et al., 2010).
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2.2. Data
2.2.1. Climate data
The temperature and precipitation data used in this article are from records collected and processed by the
National Meteorological Center of the China Meteorological Administration. This data set includes measurements
from 108 meteorological stations distributed throughout
the study area from 1951 to the present. Because some
stations were removed (Zhou et al., 2004), we use the
temperature [daily maximum (T max ), minimum (T min )
and mean (T mean ) land surface air temperatures] and
annual precipitation data from 95 stations for the period
from 1955 to 2007 in this study (Figure 1).
2.2.2. Irrigation data
We used a gridded irrigation data set based on the geographical distribution of areas equipped for irrigation
around the year 2000 (Siebert et al., 2005). The data set
is of high resolution and is available at 5 arc minute
× 5 arc minute grid cells. Two country-specific indicators were computed that take into account the geo-spatial
information density, including indicator for the density of
sub-national irrigation statistics and indicator for the density of geo-spatial records. Then the map was compared
Int. J. Climatol. 34: 1181–1195 (2014)
IRRIGATION COOLING EFFECT
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Figure 4. Decadal trends for the average of the 1, 5, and 30 highest daily T max and coldest daily T min values, along with the average June–August
(JJA) T max and December–February (DJF) T min , and the mean annual T max (T max_mean ) and T min (T min_mean ) in irrigated and reference regions
on the Huang-Huai-Hai Plain of China in the period 1955–2007. Irr–Ref is the difference of temperature trends between irrigated and reference
regions. Error bars indicate 95% confidence interval based on bootstrap resampling.
to the irrigated areas of two global land-cover inventories from remote sensing data (Siebert et al., 2005). The
quality of the irrigation map is graded as ‘very good’
by the above indicators and comparison for the region
over China (Siebert et al., 2005). To assess the quality
of this irrigation data set in regional scale, we compared
records about irrigation equipment and real practices at
the agrometeorological stations in Hebei Province collected and processed by the National Meteorological Center of the China Meteorological Administration with the
irrigation data set in Hebei Province (Figure 2). The comparison showed that most of irrigation records of the 24
stations were very consistent with the irrigation data set.
There were only two stations located at Zhangbei and
Huanghua Counties that had records inconsistent with
the irrigation data set (Figure 2). For example, Huanghua
station is with irrigation in the agrometeorological station
records while the grid which is the station located is without any irrigation area at the irrigation map. In general,
this irrigation data set for the study area is considered to
be reliable.
2.2.3. Urbanization data
The gridded urbanization data of the study area was
from the 1 × 1 km land-use set of China built through
visual interpretation of Landsat TM images for 1990,
2000 and 2005 with a spatial resolution of 30 × 30 m2
(Liu et al., 2002, 2010, 2005, 2003). We use 2005 data
as an example to show the derivation of the urbanization
data and their reliability. Firstly, a total of 411 Landsat
TM images with charge-coupled device (CCD) images
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from CBERS-2 as a supplement were collected. Then
they were geo-referenced using 1 : 50 000 topographic
maps, and the root mean squared error (RMSE) of
geometric rectification was less than 1.5 pixels (or 45 m).
Secondly, according to the field validation, the accuracy
of identification for built-up land was 98% (Liu et al.,
2010). Thirdly, in order to overlay the grid data of
irrigation and urbanization, these vector land-use data
were converted into grid data with 1 × 1 km resolution
through a method which was introduced in the study of
Liu et al. (2010). This method can make the grid data
reflect area proportion for the built-up area and eliminate
the scale effect of different data sources during data
fusion (Liu et al., 2003).
2.3.
Analyses
Following the methods of Hegerl et al. (2004) and Lobell
et al. (2008), decadal trends of temperature extremes
using daily T max and T min were computed, including the
average T max on the hottest 1, 5, and 30 d (top 1, top 5
and top 30) and average T min on the coldest 1, 5, and
30 d (bottom 1, bottom 5 and bottom 30) for each year.
Average monthly and quarterly T max and T min , and average annual T max , T min and T mean (T max_mean , T min_mean
and T mean_mean ) were also computed. Linear trends for
1955–2007 for all temperature and precipitation values
were computed using ordinary least squares regression.
On the basis of bootstrap resampling (n = 1000) of the
original time series, the 95% confidence interval was
computed nonparametrically.
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Figure 5. Decadal trends from 1955 to 2007 of average monthly T max in irrigation and reference regions on the Huang-Huai-Hai Plain of China.
Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on
bootstrap resampling.
High-irrigation regions (with irrigated land fraction
of 50–100%) are designated as irrigation regions and
then low-irrigation regions (with irrigated land fraction
of 0.1–10%) are designated as reference regions (Bonfils
and Lobell, 2007; Lobell et al., 2008).
High-urbanization regions (with built-up land fraction
of 70–100%) are designated as urbanized regions and
then low-urbanization regions (with built-up land fraction
<30%) are designated as reference regions.
3.
Results and discussion
3.1. Decadal trends of temperature under different
irrigation levels
Decadal trends of the average T max on the hottest 1, 5
and 30 d (top 1, top 5 and top 30) and average T min on
the coldest 1, 5 and 30 d (bottom 1, bottom 5 and bottom
30), along with the average annual T max , T min and T mean
(T max_mean , T min_mean and T mean_mean ) for the period
1955–2007 are shown in Figure 3. For the extremes of
daily maximum temperature (T max ), the cooling effect of
increased irrigation on the top 1, top 5 and top 30 was
similar and the trends for these were distinguishable from
that for T max_mean . The greatest decrease of temperature
was in the region irrigated up to 60–70% for top 1
(−0.29 ◦ C decade−1 ), top 5 (−0.25 ◦ C decade−1 ) and
top 30 (−0.23 ◦ C decade−1 ). The effect of irrigation
can be attributed to the enhanced evaporative cooling
and increase of soil moisture, which causes a decrease
in sensible heat flux during daytime through shifts in
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the Bowen ratio from sensible heating (Bonfils and
Lobell, 2007; Kueppers et al., 2007; Sacks et al., 2009).
However, the stations located in 50–60% irrigation
have not much cooling effect (nearly 0 ◦ C decade−1 ),
and those located in 70–80% irrigation had a smaller
cooling effect than 60–70% irrigation. There are two
possible reasons for this phenomenon. First, it may be,
in some part, a result of urbanization occurs in the
region with 50–60% irrigation (Shi et al. 2013), which
could increase the temperature trends. Shi et al. (2013)
showed that more urbanization occurred in the region
with 50–60% irrigation, compared to the region with any
other percentages of irrigation in Huang-Huai-Hai Plain.
Second, the locations with 70–80% irrigation area (only
two locations) were few compared to those with 50–60
and 60–70% irrigation (11 and 13 locations, respectively;
Figure 3).
In contrast to the cooling effect on T max , a warming
effect of increased irrigation for the lowest 1 (bottom 1),
5 (bottom 5) and 30 (bottom 30) temperatures was shown.
The greatest increase of temperature was with 60–70%
irrigation for bottom 1 (1.30 ◦ C decade−1 ), bottom 5
(1.18 ◦ C decade−1 ) and bottom 30 (0.92 ◦ C decade−1 ).
It appears that irrigation had a greater warming effect on
extreme T min than the cooling effect on extreme T max
values. A similar irrigation impact, which was much
larger during the day than at night, was also shown
by Bonfils and Lobell (2007) for research in California
and Nebraska, USA. The mechanism for the warming
effect on T min can be explained as follows. During
daytime, irrigation increases evaporation (Boucher et al.,
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IRRIGATION COOLING EFFECT
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Figure 6. Decadal trends from 1955 to 2007 of average seasonal T max in irrigated and reference regions on the Huang-Huai-Hai Plain of China.
Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on
bootstrap resampling.
2004) and the heat capacity of the soil and vegetation
(Kalnay and Cai, 2003; Bonfils and Lobell, 2007), and
moist ground and vegetation also absorb solar energy
on cloudless days (Christy et al., 2006). The energy
absorbed during the day can be released at night and
release the energy in the evening (Christy et al., 2006).
Increased level of irrigation had no clear effect on the
decadal change of annual T min (T min_mean ) and T mean
(T mean_mean ) (Figure 3).
3.2. Comparisons of extreme temperature between
irrigated and reference regions
Figure 4 shows decadal trends of average T max on the
hottest 1, 5 and 30 d and average T min on the coldest
1, 5 and 30 d, along with the averages for June–August
T max (JJA) and December–February T min (DJF) and the
average annual T max (T max_mean ) and T min (T min_mean ).
Decadal temperature changes for top 1, top 5 and top
30 and the June–August (summer) period of T max were
negative for irrigated areas. On average, differences
between the irrigated and reference regions were −0.20,
−0.18, −0.17, −0.12 and −0.07 ◦ C decade−1 for top 1,
top 5, top 30, summer (JJA) and annual T max (T max_mean ),
respectively. Independent samples t-test showed that a
significant irrigation effect was observed for the top 1
(P = 0.037), top 5 (P = 0.040) and top 30 (P = 0.040),
but not for the average for summer and average annual
T max (T max_mean ). This was in accordance with our
expectation that irrigation would have larger effects on
hot days when sensible heat fluxes were higher (Lobell
et al., 2008). The changes of lands to irrigated agriculture
areas have the potential to alter surface temperature by
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modifying the energy budget at the land–atmosphere
interface (Kueppers et al., 2008). A regional climate
model showed that a regional irrigation cooling effect
exists (Kueppers et al., 2007). Jin and Miller (2010)
explain that irrigation lowers surface temperature, which
in turn reduces the sensible heat flux during the day,
is caused by stronger surface evaporation as a result of
manually adding water during irrigation.
Trends for T min in irrigated regions were all positive
(0.40–1.16 ◦ C decade−1 ). The difference between irrigated and reference areas was largest for bottom 1 (0.43
◦
C decade−1 , P = 0.03). Although the similar differences
were shown for bottom 5 and bottom 30, they were
not significant. The finding of warming trend for T min
is consistent with previous modelling studies of irrigation effects (Christy et al., 2006; Lobell et al., 2006a).
For example, Lobell et al. (2006a) showed that latent
heat fluxes peak during the daytime, so the cooling
effect was much stronger for daily maximum than minimum temperatures. This is because the primary cause
of local cooling in irrigation was an increase in latent
heat flux and the associated decrease in sensible heat
(Lobell et al., 2006a). Christy et al. (2006) also indicated that the warming trend of minimum temperatures at a highly significant rate in all seasons in the
California Central Valley is likely related to irrigation
in their modelling results. However, there also some
other modelling study shows that irrigation also produces
stronger surface evaporation, which cools the surface,
weakens the sensible heat flux, and further dominates
the nighttime surface cooling process (Jin and Miller,
2010).
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Figure 7. Decadal trends from 1955 to 2007 of average monthly T min in irrigation and reference regions on the Huang-Huai-Hai Plain of China.
Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on
bootstrap resampling.
Figure 8. Decadal trends from 1955 to 2007 of average seasonal T min for irrigated and reference regions on the Huang-Huai-Hai Plain of China.
Irr–Ref is the difference of temperature trends between irrigated and reference regions. Error bars indicate 95% confidence interval based on
bootstrap resampling.
3.3. Seasonal variations of the irrigation effect on
extreme temperature
Figure 5 shows the decadal trends of average monthly
T max from 1955 to 2007 for irrigated and reference
regions. The cooling irrigation effect occurred only in
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June, July and August, with trends of −0.06, −0.11 and
−0.05 ◦ C decade−1 , respectively. From the differences of
decadal trends of average monthly T max between irrigated
and reference regions (Irr–Ref), the decadal trends of
irrigated regions were lower than reference regions for
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all months except for February. The most significant
differences were in June and July, with cooling of −0.14
and −0.16 ◦ C decade−1 , respectively.
The irrigation cooling effect on T max only occurred
in summer but not in the other seasons with stations
in the irrigated regions colder on average than reference
regions by 0.12 ◦ C decade−1 in summer (Figure 6). This
can be explained by the possibility that higher sensible
heat fluxes at summer days can induce larger cooling
effects on hot days (Lobell et al., 2008). The result of
irrigation cooling effect on T max in this study differed
a little from results of research in a similar location in
China undertaken by Bonfils and Lobell (2007), which
showed that T max only responded modestly with summer
time irrigation trend. Bonfils and Lobell (2007) found
for Eastern China that a cooling effect on T max was
associated with increasing irrigation fraction in summer
from 0.1–10 to 30–40%, but there was no further cooling
with the increase of irrigation area to 40–50%. A possible
explanation is that Bonfils and Lobell (2007) used the
CRU2.1 data set, in which the underlying station network
had considerably lower resolution than the data set we
used.
The warming trends for the average monthly T min
occurred in every month for both irrigated and reference
regions from 1955 to 2007. Except for February and
October, the warming trends were larger in reference
regions than those in irrigated regions for all months
(Figure 7). Jin and Miller (2010) also shows that T min has
a positive trend for all seasons and is consistent with the
observed global warming trend. Warming trends of T min
were lower in irrigated regions than those in reference
regions for all seasons and the greatest difference between
irrigated and reference regions occurred in summer
with 0.08 ◦ C decade−1 (Figure 8). The difference with
seasons between irrigated and reference regions were not
significant according to the independent samples t-tests.
3.4. Spatial analysis of the irrigation effect
On the basis of the above analysis of the irrigation
effect on T max and T min , top 1, 5, and 30 and bottom 1
provided the best indicators of irrigation related cooling
or warming in the study area. As the top 1, top 5 and
top 30 have similar spatial distributions, only the decadal
trend of the top 5 with irrigation spatial pattern is shown
(Figure 9). The decadal trend of the bottom 1 with
irrigation spatial pattern is shown in Figure 10. Most
of the stations located in irrigated grid cells (fraction of
50–100%) had larger cooling trends for extreme T max
and larger warming trends for extreme T min , while in the
reference grid cells (fractions of 0.1–10%), the trend was
opposite. However, a few stations located in the irrigated
areas showed warming trends for extreme daily maximum
temperature. This could be attributed to urbanization
which will be discussed in the Section 3.5.
Although the difference of bottom 1 at irrigated
and reference stations are significant (P < 0.05), the
spatial distribution of bottom 1 with irrigation shows
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not much strong relationship between bottom 1 and
irrigation as extreme T max . This is because most of the
decadal trends for bottom 1 are warmer than zero, and
different factors affect the extent of warming, including
greenhouse gases, clouds, local land-use patterns and
so on.
The climate regime is important to the overall impacts
of irrigation on temperature. Decadal trends of precipitation indicate that the drier north of the Huang-Huai-Hai
Plain has become drier and the wetter south has become
wetter from 1955 to 2007 (Figure 11). In order to determine the impacts of the average annual precipitation and
the decadal trends of annual precipitation on irrigation
cooling, we selected 26 irrigated stations (fraction of
50–100%), and divided them into three groups according
to the average annual precipitation and the decadal trends
of annual precipitation for 1955–2007. Stations at drier
locations (300–600 mm and 600–900 mm) cooled more
than those at wetter locations (900–1200 mm; Figure
12(a)). Stations with decreasing decadal trends of annual
precipitation showed larger irrigation cooling effects than
those with increasing trends (Figure 12(b)). The regional
climate model showed that the expansion of irrigation in
regions with low precipitation can increase the irrigation
cooling effect, and the irrigation cooling effect is most
pronounced during warm, dry times of the year, and during relatively warm, dry years (Kueppers et al., 2007,
2008).
3.5.
Spatial analysis of the urbanization effect
There were some observations located in the irrigated cell
areas that had warming trends for extreme temperature
(Figure 9). On the basis of the trend analysis for the
irrigation effect on temperatures, urbanization impacts
on extreme high-temperature events were analysed. In
order to detect if urbanization had counteracted the
irrigation cooling effect, the spatial distribution of builtup land and cropland in 1990, 2000 and 2005 are plotted
together with top 5 T max . Because they are very similar
in 1990, 2000 and 2005, only the spatial distributions
of built-up land and cropland in 2005 are shown in
Figure 13(a) and (b). It is noted that a few of stations
(for example, Beijing station) with higher urbanization
levels (Figure 13(a)) located in high-cropland regions
(Figure 13(b)) and high-irrigation regions (Figure 9) have
had larger warming trends compared to stations where
lower urbanization with high irrigation have occurred.
Stations in high-irrigation area fractions that showed
warming trends were always located in provincial or
developed cities (Figure 13). For example, the trend of
the station in Beijing was for an increase of 0.07–0.09
◦
C decade−1 for top 1, top 5 and top 30. Due to the
rapid urbanization associated with industrialization, the
creation of an UHI was a likely cause for the observed
warming at the meteorological station in Beijing (Zhou
et al., 2004).
Int. J. Climatol. 34: 1181–1195 (2014)
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W. SHI et al.
Figure 9. Spatial distribution of percent irrigation areas, along with decadal trends (◦ C decade−1 ) of the top 5 extreme maximum temperatures
(T max ) for the 95 meteorological stations on the Huang-Huai-Hai Plain of China. Pink triangles represent decreasing trends and red circles
represent increasing decadal trends of temperature.
3.6. Comparison of irrigation cooling and urbanization
warming effects
We selected 26 irrigated stations (fraction of 50–100%),
and divided them into three groups of 7 urbanized stations
(more than 70% built-up areas in 1 × 1 km grid), 3 midurbanized stations (30–70% built-up areas in 1 × 1 km
grid) and 16 reference stations (less than 30% built-up
areas in 1 × 1 km grid). Decadal trends for the top 1, top
5 and top 30 of the first and the third group are compared
in Figure 14, which indicates that urbanization weakened
the irrigation cooling effect. For example, for top 1, top
5 and top 30 the decadal trends of irrigated stations
with urbanization decreased 0.04–0.09 ◦ C decade−1 , but
decreased 0.19–0.21 ◦ C decade−1 for reference regions.
The differences between the urbanized and reference
region of decadal trends for top 1, top 5 and top 30
varied from 0.12 to 0.16 ◦ C. These urbanization warming
 2013 Royal Meteorological Society
effects were not as strong as we expected relative to the
irrigation cooling effect, so that the average warming
values of urbanized stations for top 1, top 5 and top
30 were modified by irrigation cooling effects during
1955–2007 (Figure 14). Kalnay and Cai (2003) have
shown that the urban impacts on temperature are much
less than those of other large-scale land-use changes in
the United States and may be more easily compensated
by irrigation cooling effects.
To explain the roles of irrigation versus urbanization
better, we compared two groups of stations with different
fractions of irrigation and urbanization: (1) irrigation
(more than 50%) with urbanization (more than 70%;
Irr and Urb), and (2) irrigation (more than 50%) with
less urbanization (less than 30%; Irr and Ref; Figure
15). Greater cooling effect (top 1 for −0.14 ◦ C, top 5
for −0.19 ◦ C and top 30 for −0.18 ◦ C) occurred for
the Irr and Ref stations. The cooling effect was also
Int. J. Climatol. 34: 1181–1195 (2014)
IRRIGATION COOLING EFFECT
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Figure 10. Spatial distribution of percent irrigation areas, along with decadal trends (◦ C decade−1 ) of the bottom 1 extreme minimum temperature
(T min ) for the 95 meteorological stations on Huang-Huai-Hai Plain of China. Blue triangles represent decreasing trends and red circles represent
increasing decadal trends of temperature.
shown for the Irr and Urb stations (top 1 for −0.09 ◦ C,
top 5 for −0.07 ◦ C and top 30 for −0.05 ◦ C) but was
smaller than the Irr and Ref stations. This suggests that
the T max is affected by interaction of urbanization and
irrigation, but the effect of urbanization may be less than
that of irrigation. In addition, if irrigated area declines
due to lack of sufficient water supply or conversion of
irrigated agriculture to urban land, the temperature will
be increasing because greenhouse gas-driven warming
may be reinforced by regional land-use change (Kueppers
et al., 2007).
4. Conclusions
There has been a very large increase of both irrigation and
urbanization on the Huang-Huai-Hai Plain of China since
1955. This study comprehensively analysis the effects
of these two processes on surface temperatures on the
Plain. Our results, as significantly tested by independent
 2013 Royal Meteorological Society
samples t-tests, suggest that irrigation has cooled extreme
daily maximum temperatures and warmed extreme daily
minimum temperatures. For example, the average values
of daily maximum temperature for the hottest 1, 5,
and 30 d at irrigated stations were colder by 0.17–0.20
◦
C decade−1 in irrigated regions than in reference regions
of the study area. However, only the daily minimum
temperature on the coldest 1 d at irrigated stations was
obviously warmer than that at reference regions by
0.43 ◦ C decade−1 . Cooling of extreme daily maximum
temperatures is particularly noted in the summer months
of June, July and August. On the basis of the findings, it
is concluded that irrigation generally modulates extreme
maximum temperatures of the hottest days of the year. As
well, irrigation generally increases the extreme minimum
temperatures of the coldest days on the Huang-Huai-Hai
Plain of China. Moreover, high precipitation regions of
the Plain exhibit a lower irrigation cooling effect than
low precipitation regions. In regions where irrigation
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W. SHI et al.
Figure 11. Spatial distribution of average annual precipitation for 1955–2007, along with decadal trends (mm per decade) in annual precipitation
for the 95 meteorological stations on Huang-Huai-Hai Plain of China. Blue triangles represent decreasing trends and red circles represent
increasing decadal trends of precipitation.
interfaces with urbanization, urbanization warming of
extreme daily maximum temperatures appears to have
only partly counteracted the cooling effect of irrigation on
temperature. Stations in urban and irrigation regions also
depict an overall cooling trend over decades. However, as
this work is limited in spatial scale and data sample size,
the combined effect of irrigation and urbanization in their
regions of overlap requires further study. It is needed to
use modelling tools to isolate the role of irrigation and
urbanization from that of climate change to qualify the
detailed physical processes of the impact of the former
two events.
It is important to note that the differences in temperature between irrigated and reference regions are induced
by other factors as well. For example, variations in climate regimes, clouds, aerosol and land characteristics
 2013 Royal Meteorological Society
(elevation, slope, aspect, latitude and proximity to the
sea) also obfuscate the atmospheric signal, especially
when its spatial pattern correlates with that of irrigation. Further studies should therefore be conducted to
quantify the net impact of irrigation on regional climate
dynamics.
This study sheds further light on the important issue of
uncertainties introduced by the effect of land-use change
on regional climate dynamics. Relationships among irrigation, urbanization, precipitation and other climatic variables could be useful in studying the land-state processes in the Huang-Huai-Hai Plain. On the other hand,
the effects of land-use change, such as deforestation,
grazing and abandonment of barren lands on regional
climate dynamics may provide additional insight into
land-state process in other regions of the world. Detailed
Int. J. Climatol. 34: 1181–1195 (2014)
IRRIGATION COOLING EFFECT
1193
Figure 14. The comparisons of decadal trends of top 1, top 5 and top 30
for irrigated stations with urbanization (Urb, 70–100% urbanization)
and reference (Ref, less than 30% urbanization) on the Huang-Huai-Hai
Plain of China. Urb–Ref is the difference between Urb and Ref. Error
bars indicate 95% confidence interval based on bootstrap resampling.
effects of irrigation and urbanization on climate change
should not only use observational data, but should also
be coupled with dynamic land-use models and regional
climate models to understand the complex processes and
controlling mechanisms.
Figure 12. Impacts of (a) average annual precipitation and (b) decadal
trends in annual precipitation for 1955–2007 on decadal trends of irrigation cooling effects for the top 1, top 5 and top 30 extreme maximum
temperatures (T max ) on the Huang-Huai-Hai Plain of China. Error bars
indicate 95% confidence interval based on bootstrap resampling.
model simulations of land use and regional climate are
also needed to foster even deeper understandings of the
dynamics of temperature, precipitation and other agroclimatic and human factors. Further efforts to explain the
Acknowledgements
This research was supported by the National Basic
Research Program of China (2010CB950902), the
National Natural Science Foundation of China (41001057
and 41071030) and the ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences, Climate
Change: Carbon Budget and Relevant Issues grant
no. XDA05090310. We gratefully acknowledge Prof.
Figure 13. Spatial distribution of (a) built-up area (urban) and (b) cropland fraction on a 1 × 1 km grid for the Huang-Huai-Hai Plain in 2005
together with the top 5 of T max . The value indicates the percentage of built-up area (cropland) within a 1 × 1 km grid cell.
 2013 Royal Meteorological Society
Int. J. Climatol. 34: 1181–1195 (2014)
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W. SHI et al.
Figure 15. The comparisons of the two groups of stations for decadal trends of top 1, top 5 and top 30 on the Huang-Huai-Hai Plain of China.
Irr and Urb represents the stations are not only in irrigation region but also urbanization region, and Irr and Ref represents the stations are in
irrigation region but without urbanization. Error bars indicate 95% confidence interval based on bootstrap resampling.
Warwick Harris (Landcare Research, New Zealand), Dr
Juana Paul Moiwo (IGSNRR, CAS), the anonymous
reviewers and the editor for their insightful comments,
suggestions and language revisions.
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